Knowledge (i.e. information that means something) is a key asset to an organisation and exploiting the knowledge within an organisation is becoming more and more important. Knowledge management allows capture, storage and analysis of information flowing within an organisation. The popularity of electronic communication mechanisms such as electronic mail, instant messaging, newsgroups etc. has become more widespread as they allow remote users to exchange information via electronic messages. These electronic messages can be captured and stored to provide a repository of information that can be analysed in order to capture knowledge.
Analysis can be carried out in many ways. In one example, the electronic messages exchanged between entities (e.g. individuals, computers etc.) in an electronic network, can be analysed in order to capture knowledge. Information associated with the entities and associated electronic messages represent a knowledge network. In FIG. 1, the knowledge network is visually represented. Each entity is represented as a node (i.e. A-J) and an electronic message sent by an entity is represented as an arrow, wherein the arrowhead represents the direction of sending. It can be seen that several electronic messages between entities A and B, B and F, I and J, have been exchanged and this implies strong relationships between those entities. In another example, the frequency of electronic messages between entities can be analysed in order to further analyse strengths of relationships between entities. It can also be seen that that entity A has an important role in the knowledge network, because of the number of other entities (i.e. B, C, D, E and F) communicating with entity A. It can also be seen that the knowledge network comprises two sub-networks, a first sub-network comprising entities A, B, C, D, E, F, G, and H and another sub-network comprising entities I and J.
It should be understood that there are disadvantages associated with these analysis mechanisms. In an electronic network, some of the electronic messages being exchanged may be trivial and meaningless for a particular type of knowledge capture. For example, entity A may have sent and received electronic messages in an erroneous broadcast. Yet from FIG. 1, it had previously been deduced that entity A plays an important role in the knowledge network. In another example, communication between members of a group is found to be as frequent as communication between members of the group and individuals outside of the group. The communication within the group is in fact due to social aspects (e.g. jokes, arranging social gatherings etc.) and the communication between members of the group and individuals outside of the group is due to the expertise of the group members (e.g. the group members work at a call centre and deal with customer calls). In order to capture knowledge relating to expertise, it is the latter communication that is important. However, this is masked by the inter-group communications.
It is therefore difficult to capture relevant knowledge from all the communications that are occurring in an electronic network. One prior art mechanism analyses the content of electronic messages being exchanged between individuals. This provides knowledge regarding the topics of interest that are being communicated. However, this mechanism has an associated performance overhead. Another prior art mechanism applies one or more filters to captured information in order to filter out non-relevant information, see for example US. 2003/0084053.